Proceedings of the Annual Meeting of the Cognitive Science Society

There has been a small but now growing interest in studying decision making in real-world contexts where part of the problem faced by decision makers is to generate candidate options they will actually decide between. While some of this work has employed large decision spaces where options are discrete and valuation is computationally tractable (e.g., chess), very little work has focused on genuinely open-ended decision contexts that more closely mirror mundane real-world decisions. This paper leverages large language models to investigate how people generate options when facing genuinely open-ended problems. Across three experiments, we apply semantic similarity and sentiment analyses to the options that participants sequentially generate for real-world decision problems. We find that the first options generated tend to be sampled from a relatively local region of semantic space and are typically of high value. As additional options are generated, they become increasingly dissimilar and are of lower value. These patterns held both at the level of individual option generation trajectories within a given participant and at the level of individual differences across participants.